• Kumud

We present a new unsupervised classification algorithm hierarchical construction of dynamic graphs called Advanced Ant Colony Algorithm (AACA). It uses the principle of the chemical recognition system observed in real ants applied on Cluster graphs [1,2]. We detail the algorithm and local rules of ant behavior artifacts and the resulting evolution on the cluster graph from the study that was carried out on the mechanisms fundamentals of the chemical recognition system in real ants. We also described all the tests that were used to validate the AACA algorithm, studying in particular the results obtained on games of real digital and binary data, as well as the evaluation indices so judge the quality of the classification obtained. In the display part of the graphs generated, we will present the neighborhood graphs of some databases tested by the algorithm AACA. We conclude by presenting the interests of this approach as well as the extensions possible to propose.

"Advanced Ant Colony Algorithm based on unsupervised classification for the construction of dynamic graph", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.117-123, June 2019, Available :http://www.jetir.org/papers/JETIR1908018.pdf

"Advanced Ant Colony Algorithm based on unsupervised classification for the construction of dynamic graph", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp117-123, June 2019, Available at : http://www.jetir.org/papers/JETIR1908018.pdf